Are Excipients Really Inert Ingredients? A Review of Adverse Reactions to Excipients in Oral Dermatologic Medications in Canada
Bibliographic record
Abstract
BACKGROUND: Whereas several literature reviews have discussed the role of excipients in drug-related reactions, no article has focused specifically on those found in oral dermatologic medications. METHODS: The Compendium of Pharmaceuticals and Specialties (CPS) was used to reference the inert ingredients found in oral dermatologic medications. An extensive literature review was subsequently conducted using PubMed and MEDLINE to document adverse reactions to these excipients. RESULTS: Sixty-three oral dermatologic medications were reviewed. Lactose was commonly used as a filler. Several medications indicated that they were dye, tartrazine, or gluten free. Three medications were found to contain soybean oil and one was found to contain peanut oil. CONCLUSIONS: Although there are documented reactions to excipients in other products in the literature, few reports outline reactions to excipients in oral dermatologic medications. Whether this low frequency is accurate or whether it is due to a lack of reporting remains unknown. If the latter reasoning is correct, dermatologists must be more aware of these possible reactions. This article serves as a reference guide for dermatologists to aid in prescribing medications to individuals with known sensitivities and to assist in working up patients with suspected reactions to inert ingredients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".